In this work, inspired by recent effort of using DNN in inertial navigation algorithms [1]}, [2]}, [3]}, [4]}, [5]}, we propose a novel data driven method for the inertial sensor modeling to achieve enhanced performance.
In particular, we design a recurrent neural network (RNN) based method to process raw gyroscope and accelerometer measurements to reduce the modeling errors and allow improved sensor fusion.
To ensure generality of our approach under different use cases,
we train our network by regressing the observable IMU motion terms, instead of the relative IMU position and orientation that are adopted in most literature [1]}, [4]}. Additionally, we propose a dedicated method on processing IMU measurements, including multiple loss functions, training strategies, and data preparation (data pre-processing, augmentation, and so on), which are critical in
applying our method for high-precision inertial aided navigation.
To validate the effectiveness of the proposed method, extensive real-world experiments are also conducted in this work, by utilizing the datasets collected from automotive vehicles [8]}, drones [9]}, and ground robots. The results show that the proposed method outperforms the various competing methods, including both traditional and DNN based ones [10]}, [5]}, [12]}, [13]}, [14]}, by wide margins.
